Design and Monitoring of Adaptive Clinical Trials
Decision Point
Indicators: The colored circles (green and red) next to each decision
point likely indicate a binary decision-making outcome:
SAD (Single Ascending Dose) and MAD (Multiple Ascending Dose)
The SAD/MAD dose escalation strategy is crucial for:
Key Considerations in Dose Escalation
1. Is there a
range of safe doses where we can explore efficacy? - Yes, the
therapeutic window shown indicates a range of doses where the efficacy
is high and toxicity is minimal. This range allows for the exploration
of optimal dosing to maximize therapeutic benefits while minimizing
adverse effects.
The dose escalation process is crucial in clinical trials for several reasons:
Methodology of a dose escalation process in clinical trials.
Early Phase Decision-Making Stages
Decision Making Approaches
By employing adaptive designs, Bayesian methods, and biomarker-based endpoints, clinical trials can become more flexible and responsive to emerging data, potentially reducing development times and improving patient outcomes. These strategies also align well with modern regulatory frameworks that encourage innovation and efficiency in drug development.
Decision Framework
Combination therapies involve the use of two or more treatments simultaneously to achieve better efficacy than any single treatment alone. This approach is common in various therapeutic areas, including oncology, infectious diseases, and chronic conditions like diabetes and hypertension.
Dose escalation with dual agents involves complex strategies that are designed to optimize the effectiveness and safety of two drugs given in combination. This method is commonly used in cancer therapy, where two potentially complementary drugs are administered to achieve better clinical outcomes than could be achieved by either drug alone.
Specific Models and Methods
The concept of a Seamless Phase 2a/b Combination Design in clinical trials represents an innovative approach to streamline drug development by integrating what are traditionally two separate phases into a single, continuous study. This design allows for the more efficient evaluation of a therapeutic candidate, reducing the time and cost associated with transitioning between phases.
Typical Applications
Components of the Seamless Phase 2a/b Combination Design
Key Features and Methodologies
Overview of the Design
Key Features and Methodologies
The Seamless Phase 2b/3 Study Design is an innovative clinical trial strategy that combines Phase 2b (often the dose confirmation phase) and Phase 3 (pivotal trials for efficacy and safety) into a single, continuous protocol. This design allows for a more fluid transition between the late-stage clinical development phases, reducing delays, and potentially accelerating the drug approval process.
Key Features of Seamless Phase 2b/3 Study Design
Example Studies:
An Enrichment Strategy in clinical trials is designed to enhance the probability of demonstrating a treatment effect by selecting patients who are more likely to respond to the treatment based on predefined criteria. This approach can be particularly beneficial when there’s a clear hypothesis that a specific subgroup of the population may exhibit a stronger response or better tolerance to the treatment.
The TAPPAS trial provides an illustrative example of applying adaptive design principles in a clinical trial setting, particularly for treating a rare cancer with limited treatment options.
Angiosarcoma and Current Treatments
Combination Therapy in the TAPPAS Trial
Trial Objectives
Population Focus
Adaptive Design Justification
The RALES (Randomized Aldactone Evaluation Study) trial was a significant clinical study focusing on the effectiveness of an aldosterone receptor blocker compared to a placebo.
The Psoriasis trial example involves: - Primary Endpoint: Achievement of PASI-75 by week 16, which measures improvement in psoriasis. - Design Parameters: Designed for 95% power to detect a 10% improvement with a new treatment relative to placebo, with uncertainty about the placebo response rate (π_c = 7.5%).
The problem here is that the power of the trial depends on both the actual placebo response rate (π_c) and the effect size (δ), which can be unknown and vary. If π_c or δ are misestimated, it can impact the trial’s power, making the originally calculated sample size insufficient or excessive.
By using an information-based design, the trial is allowed to adapt by recalculating the necessary sample size based on accruing data about the actual placebo rate and effect size. This can be done through interim analyses, where the actual information accrued (J_j) is compared against the pre-specified maximum information \(I_{\text{max}}\). If \(J_j\) meets or exceeds \(I_{\text{max}}\), or efficacy boundaries are crossed, the trial might be stopped early for efficacy or futility, or the sample size adjusted to meet the desired power.
This approach proposes using “statistical information” rather than fixed sample sizes to guide the monitoring and conclusion of clinical trials. The rationale here is to accumulate enough information to make robust statistical decisions, thereby potentially making the trial more efficient and flexible.
This approach is particularly beneficial in scenarios like the psoriasis trial where there is considerable uncertainty about critical parameters that influence study outcomes. It allows the study to adapt to the observed data, making it potentially more efficient and likely to reach conclusive results.
\[ I_{\text{max}} = \left( \frac{Z_{\alpha/2} + Z_{\beta}}{\delta} \right)^2 \times \text{Inflation Factor} \]
The table indicates that irrespective of the true placebo rate (π_c), the maximum statistical information \(I_{\text{max}}\) remains constant, suggesting the sample size (N_max) adjusts according to the variability observed due to π_c.
\[ J_j = \left[ \text{se}(\delta)^{-1} \right]^2 \left[ \frac{\hat{\pi}_c (1 - \hat{\pi}_c) + \hat{\pi}_e (1 - \hat{\pi}_e)}{N/2} \right]^{-1/2} \] - se(δ)^{-1: Represents the precision (or inverse of the standard error) of the estimated treatment effect. - N/2: Assumes an equal split of the sample size between treatment and control groups. - π_e and π_c: Estimated rates of the endpoint for the experimental and control groups, respectively.
Sample size re-estimation (SSR) in clinical trials is a strategic approach employed when initial assumptions about a study need adjustment based on interim data. This can be crucial for ensuring the scientific validity and efficiency of a trial.
During interim analysis, questions might arise such as: - Should the study continue to target a difference (δ) of 2 points? - Is the assumed standard deviation (σ = 7.5) still valid?
To address these questions: - Conditional Power (CP): This is the probability that the study will detect the predefined effect size, given the interim results. Adjustments might be made to increase the sample size to enhance CP. - Adjusting Critical Cut-off: To maintain the integrity of the type-1 error rate, the critical cut-off value for stopping the trial might need adjustment.
Increasing the sample size can boost CP because it typically reduces the variance of the test statistic, making it more likely that \(Z_2\) will exceed \(c\).
This adjustment ensures that even with an altered trial design, the integrity of the study’s conclusions remains sound. The statistical methodology aims to maintain the trial’s power (ability to detect a true effect) without compromising its rigor due to potential overestimation of the type-1 error.
Cap on Increases: Often, increases in sample size are capped (e.g., no more than double the initial size) to prevent logistical and financial overextension.
Zones of Adjustment:
Placebo Response and Treatment Effect: The expected placebo response rate is 35%, with an anticipated 20% improvement with crofelemer treatment. These assumptions are critical for calculating the necessary sample size and for power calculations to ensure the study is adequately powered to detect a clinically meaningful effect.
Implications for Sample Size Re-Estimation: Given the uncertainty in the optimal dose and variability in the placebo response, an adaptive trial design with sample size re-estimation could be considered. This approach would allow adjustments based on interim analysis results, potentially optimizing the study design in real-time to ensure sufficient power and minimize unnecessary exposure to less effective doses.
Interim Analyses: Conducting interim analyses would allow for the assessment of preliminary efficacy and safety data. Based on these data, decisions could be made about continuing, modifying, or stopping the trial for futility or efficacy.
Adjustments Based on Conditional Power: If interim results suggest changes in the estimated placebo response or differentially greater efficacy at specific doses, the sample size could be adjusted to ensure that the study remains adequately powered to detect significant treatment effects.
1. Inverse Normal Combination of Stage 1 and Stage 2 p-values
This method combines the p-values from different stages of the study using a weighted Z-transform approach. The formula provided:
\[ Z_2 = \sqrt{\frac{n_1}{n_1 + n(2)}} \phi^{-1}(1 - p_1) + \sqrt{\frac{n(2)}{n_1 + n(2)}} \phi^{-1}(1 - p_2) \]
This method assumes that combining information across stages can lead to a more powerful test while still controlling for Type I error, provided the combination rule is properly calibrated.
2. Closed Testing
Closed testing is a rigorous method for controlling FWER in the context of multiple hypothesis testing, especially when tests are not independent.
Features of MAMS:
Multiple Treatment Arms: Involves comparing several treatment options against a common control group, allowing simultaneous evaluation of multiple interventions.
Multiple Interim Analyses: Scheduled assessments of the accumulating data at multiple points during the trial. These interim looks allow for early decisions about the continuation, modification, or termination of treatment arms.
Early Stopping Rules: The trial can be stopped early for efficacy if a treatment shows clear benefit, or for futility if it’s unlikely to show benefit by the end of the study.
Continuation with Multiple Winners: Unlike traditional designs that might stop after finding one effective treatment, MAMS design can continue to evaluate other promising treatments.
Dropping Losers: Ineffective treatment arms can be discontinued at interim stages, focusing resources on more promising treatments.
Dose Selection: Flexibility to adjust doses or select the most effective dose based on interim results.
Sample Size Re-estimation (SSR): Sample sizes can be recalculated based on interim data to ensure adequate power is maintained throughout the trial, especially useful if initial estimates of effect size (δ) or variability (σ) are inaccurate.
Control of Type-1 Error: Despite the complexity and multiple hypothesis testing involved, the design includes methodologies to maintain strong control over the type-1 error rate, ensuring the validity of the trial’s conclusions.
Trial Details: - Intervention: Evaluated three doses of Variciguate compared to placebo. - Primary Endpoint: Week-12 reduction in the log of NT-proBNP, a biomarker used to assess heart function and heart failure. - Sample Size and Power: A total of 388 patients to achieve 80% power for detecting a change of δ = 0.187 in the log NT-proBNP, assuming a standard deviation (σ) of 0.52.
Adaptive Features: - Adaptive Design Considerations: The trial was prepared to adjust for different values of δ and σ than initially estimated, which is crucial if the biological effect of Variciguate or the variability in NT-proBNP measurements was misestimated. - Interim Analyses with SSR and Drop the Loser: The design included provisions for interim analyses to reassess the continued relevance of each dose. Less promising doses could be dropped (‘Drop the Loser’), and the sample size could be recalculated based on the data gathered to that point (‘SSR’).
j), where
i denotes the treatment arm.j.Treatment Arms: - TRC105 + Pazopanib: TRC105 targets the endoglin receptor and is combined with Pazopanib, which targets the VEGF receptor. - Pazopanib Alone: Standard of care, serving as a control.
Subgroups: - Two primary subgroups, cutaneous and visceral. The cutaneous subgroup is notably more sensitive to TRC105, suggesting a potential for subgroup-specific efficacy.
Interim Decisions Based on Interim Analysis: - Favorable Results: If the interim results are favorable, the trial continues as planned. - Promising but Uncertain Results: If results are promising but not conclusively favorable, the trial may adapt by increasing the sample size to enhance statistical power. - Unfavorable Results for Combined Therapy: The trial continues as planned or stops for futility based on specific interim findings. - Population Enrichment: If the interim results suggest that the cutaneous subgroup is particularly responsive, the trial may shift its focus to this subgroup, enriching the patient population to those most likely to benefit.
Statistical Methodology for Decision Making:
1. In Case of No Enrichment
When there is no enrichment, i.e., the trial continues with the full patient population, the significance is declared if: \[ w_1 \Phi^{-1}(1 - p_1^{FS}) + w_2 \Phi^{-1}(1 - p_2^{FS}) \geq Z_{\alpha} \] \[ w_1 \Phi^{-1}(1 - p_1^F) + w_2 \Phi^{-1}(1 - p_2^F) \geq Z_{\alpha} \]
Where: - \(\Phi^{-1}\) is the inverse of the standard normal cumulative distribution function. - \(p_1^{FS}\) and \(p_2^{FS}\) are the p-values for the full sample from stages 1 and 2, respectively, after the interim analysis. - \(p_1^F\) and \(p_2^F\) are the p-values for the full sample from stages 1 and 2, respectively, before the interim analysis. - \(w_1\) and \(w_2\) are the weights assigned to the p-values from each respective stage. - \(Z_{\alpha}\) is the critical value from the standard normal distribution corresponding to the desired overall Type I error rate, \(\alpha\).
2. In Case of Enrichment
When the trial opts for enrichment, i.e., focusing on a specific subgroup (e.g., the cutaneous subgroup) after finding differential treatment effects, the significance is declared if: \[ w_1 \Phi^{-1}(1 - p_1^{FS}) + w_2 \Phi^{-1}(1 - p_2^{FS}) \geq Z_{\alpha} \] \[ w_1 \Phi^{-1}(1 - p_1^S) + w_2 \Phi^{-1}(1 - p_2^S) \geq Z_{\alpha} \]
Where: - \(p_1^S\) and \(p_2^S\) are the p-values from the enriched subgroup (e.g., cutaneous) from stages 1 and 2, respectively.
Recruitment is very challenging due to rare disease. Easier to start small and ask for more. Given the rarity of the disease and the challenges in recruitment:
Zone: Categorizes the possible outcomes of interim analysis into four zones: